Piggyback: Adding Multiple Tasks to a Single, Fixed Network by Learning to Mask

@article{Mallya2018PiggybackAM,
  title={Piggyback: Adding Multiple Tasks to a Single, Fixed Network by Learning to Mask},
  author={Arun Mallya and Svetlana Lazebnik},
  journal={CoRR},
  year={2018},
  volume={abs/1801.06519}
}
This work presents a method for adding multiple tasks to a single, fixed deep neural network without affecting performance on already learned tasks. By building upon concepts from network quantization and sparsification, we learn binary masks that “piggyback”, or are applied to an existing network to provide good performance on a new task. These masks are learned in an end-to-end differentiable fashion, and incur a low overhead of 1 bit per network parameter, per task. Even though the… CONTINUE READING
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